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Incorporating factual knowledge into pre-trained language models (PLM) suchas BERT is an emerging trend in recent NLP studies. However, most of theexisting methods combine the external knowledge integration module with amodified pre-training loss and re-implement the pre-training process on thelarge-scale corpus. Re-pretraining these models is usually resource-consuming,and difficult to adapt to another domain with a different knowledge graph (KG).Besides, those works either cannot embed knowledge context dynamicallyaccording to textual context or struggle with the knowledge ambiguity issue. Inthis paper, we propose a novel knowledge-aware language model framework basedon fine-tuning process, which equips PLM with a unified knowledge-enhanced textgraph that contains both text and multi-relational sub-graphs extracted fromKG. We design a hierarchical relational-graph-based message passing mechanism,which can allow the representations of injected KG and text to mutually updateeach other and can dynamically select ambiguous mentioned entities that sharethe same text. Our empirical results show that our model can efficientlyincorporate world knowledge from KGs into existing language models such asBERT, and achieve significant improvement on the machine reading comprehension(MRC) task compared with other knowledge-enhanced models.

KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs